usage

prerequisite: you need to have t5-gqa fork of transformers installed, and accelerate.

from transformers import pipeline

pipe = pipeline(
    "text2text-generation",
    model="BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2",
    device_map="auto",
)
prompt = "Write me a python fn that demonstrates an advanced sorting algorithm"
res = pipe(
    prompt, max_new_tokens=384, num_beams=4, early_stopping=True, repetition_penalty=1.1
)
print(res[0]["generated_text"])

Quick eval

Quick eval for: BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2

hf (pretrained=BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2,trust_remote_code=True,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
boolq	2	none	0	acc	↑	0.6364	±	0.0084
openbookqa	1	none	0	acc	↑	0.1480	±	0.0159
		none	0	acc_norm	↑	0.2860	±	0.0202
piqa	1	none	0	acc	↑	0.6083	±	0.0114
		none	0	acc_norm	↑	0.6132	±	0.0114
social_iqa	0	none	0	acc	↑	0.3854	±	0.0110
tinyArc	0	none	25	acc_norm	↑	0.3122	±	N/A
tinyHellaswag	0	none	10	acc_norm	↑	0.3356	±	N/A
tinyMMLU	0	none	0	acc_norm	↑	0.2793	±	N/A
winogrande	1	none	0	acc	↑	0.5201	±	0.0140

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2.5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 17868
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 32
total_train_batch_size: 256
total_eval_batch_size: 8
optimizer: Use paged_ademamix_32bit and the args are: No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_ratio: 0.02
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
1.4008	0.2534	1000	1.4020	91375832
1.3456	0.5068	2000	1.3669	182939052
1.3437	0.7602	3000	1.3378	274855796